Magnitude Sensitive Competitive Learning

This paper presents a new algorithm, Magnitude Sensitive Competitive Learning (MSCL), which has the ability of distributing the unit weights following any magnitude calculated from the unit parameters or the input data inside the Voronoi region of the unit. This controlled behavior permits to surpass other standard Competitive Learning algorithms that only tend to concentrate neurons accordingly to the input data density. Some application examples applying different magnitude functions show the MSCL possibilities.